The subject matter discussed in this section should not be assumed to be prior art merely as a result of its mention in this section. Similarly, a problem mentioned in this section or associated with the subject matter provided as background should not be assumed to have been previously recognized in the prior art. The subject matter in this section merely represents different approaches, which in and of themselves can also correspond to implementations of the claimed technology.
In recent years, there has been enormous interest in making vehicles smarter. Autonomous vehicles could free drivers of the burden of driving while enhancing vehicle safety. There has also been considerable interest in effective and reliable robotic delivery systems for handling intermittent, on-demand, or scheduled deliveries of items in a wide variety of environments. Ideally, delivery robots should be able to securely carry objects and remain stable while moving, and have a configuration that prevents object damage or loss.
For autonomous units such as autonomous vehicles and delivery robots to function properly in a wide variety of environments, sophisticated sensors capable of supporting autonomous navigation are needed. Such sensors can be used to identify and localize absolute or relative position, and detect stationary and non-stationary obstacles. Obstacle detection is particularly important for avoiding unwanted collisions.
Conventional obstacle avoidance commonly relies on long-distance rangefinders that actively scan the environment using laser, infrared, or sonar beams. While such active range finding sensor systems can provide highly accurate centimeter scale position data on millisecond timescales, they are relatively expensive. For example, laser-based sensors with a wide field of view can sense stationary or non-stationary obstacles by projecting a long-range laser beam and scanning it to bounce off any obstacles in order to detect the distance to the closest obstacle along that beam's path. The long-range laser beam effectively delivers a view of obstacles in a field of view around the sensor and provides mapping and/or obstacle avoidance data that can be used by autonomous units. But lasers are slow and can be costly. They are also subject to regulation.
Unfortunately, such sensors are costly and can be difficult to mount and position in an autonomous unit. Since sensor systems are a significant component of an autonomous unit's bill of materials, providing low-cost commercial delivery robots/autonomous vehicles depends at least in part upon use of low-cost sensor systems that are effective, rugged, and simple to calibrate and assemble.